The excellent cover estimation is very important to the payload location of JPEG image steganography. But it is still hard to exactly estimate the quantized DCT coefficients in cover JPEG image. Therefore, this paper proposes a JPEG image steganography payload location method based on optimal estimation of cover co-frequency sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub-image. The proposed method combines the coefficients of the same position in each 8 × 8 block in the JPEG image to obtain 64 co-frequency sub-images and then uses the maximum a posterior (MAP) probability algorithm to find the optimal estimations of cover co-frequency sub-images by the Markov model. Then, the residual of each DCT coefficient is obtained by computing the absolute difference between it and the estimated cover version of it, and the average residual over coefficients in the same position of multiple stego images embedded along the same path is used to estimate the stego position. The experimental results show that the proposed payload location method can significantly improve the locating accuracy of the stego positions in low frequencies.

The high-precision geolocation of Internet hosts plays an important role in many applications, such as online advertising and deception detection. The existing typical high-precision geolocation algorithms usually utilize single-hop or relative delay to geolocate an Internet host at street-level granularity. However, it is difficult to accurately measure the single-hop or relative delay within a city. This challenge sometimes results in large geolocation errors. To solve this problem, a street-level geolocation algorithm based on router multilevel partitioning is proposed. Unlike existing typical algorithms, the proposed algorithm makes a credible hypothesis that each router has a relatively stable service object for a period of time. By analyzing the connection between routers and landmarks, the possible geographic service ranges of routers are inferred from the geographic distribution of landmarks. Then, distance constraints arising from routers' service ranges are formed to estimate the geographic location of the target IP. Theoretical analysis of the geolocation error shows that the maximum and average errors of the proposed algorithm are less than those of existing typical algorithms. The proposed algorithm is evaluated by geolocating a total of 12,152 target IP addresses located in four cities in different regions. The experimental results show that, compared with the existing typical street-level geolocation algorithms SLG and NC-Geo, the average median error of the proposed algorithm decreases from 4.735 km and 3.776 km to 3.25 km, representing error reductions of approximately 31.36% and 13.96%, respectively. INDEX TERMS IP geolocation, delay-distance correlation, multilevel partitioning of routers, service range calculation.

In order to solve the problem of low efficiency and low accuracy of manually picked fast shaft collimation lens in actual production, the method of recognition of fast shaft collimation lens based on machine vision is studied. Using industrial camera to achieve image acquisition, using HALCON software to pre-process the image of fast-axis collimated lens, edge detection and other aspects of research, through the platform verification, the results show that the processed image is clear and easy to identify. In the lens placement is not accurate, the background is dirty can still ensure a better positioning effect, This study has some reference significance for visual recognition in the actual production of fast-axis collimation lens.

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